article

Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method

Authors:

Eduardo Fernandez,

Fernando Lopez,

Carlos A. Coello CoelloAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 181, Issue 1

Pages 44 - 56

https://doi.org/10.1016/j.ins.2010.09.007

Published: 01 January 2011 Publication History

Abstract

Most current approaches in the evolutionary multiobjective optimization literature concentrate on adapting an evolutionary algorithm to generate an approximation of the Pareto frontier. However, finding this set does not solve the problem. The decision-maker still has to choose the best compromise solution out of that set. Here, we introduce a new characterization of the best compromise solution of a multiobjective optimization problem. By using a relational system of preferences based on a multicriteria decision aid way of thinking, and an outranked-based dominance generalization, we derive some necessary and sufficient conditions which describe satisfactory approximations to the best compromise. Such conditions define a lexicographic minimum of a bi-objective optimization problem, which is a map of the original one. The NOSGA-II method is a NSGA-II inspired efficient way of solving the resulting mapped problem.

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Rivera GCruz-Reyes LFernandez EGomez-Santillan CRangel-Valdez N(2023)An interactive ACO enriched with an eclectic multi-criteria ordinal classifier to address many-objective optimisation problemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120813232:COnline publication date: 1-Dec-2023
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Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 181, Issue 1

January, 2011

257 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2010.

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Elsevier Science Inc.

United States

Publication History

Published: 01 January 2011

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Cited By

Rivera GCruz-Reyes LFernandez EGomez-Santillan CRangel-Valdez N(2023)An interactive ACO enriched with an eclectic multi-criteria ordinal classifier to address many-objective optimisation problemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120813232:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120813
He LNan YIshibuchi HSrinivasan D(2023)Preference-Based Nonlinear Normalization for Multiobjective OptimizationEvolutionary Multi-Criterion Optimization10.1007/978-3-031-27250-9_40(563-577)Online publication date: 20-Mar-2023
https://dl.acm.org/doi/10.1007/978-3-031-27250-9_40
Szlapczynska JSzlapczynski R(2019)Preference-based evolutionary multi-objective optimization in ship weather routingApplied Soft Computing10.1016/j.asoc.2019.10574284:COnline publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1016/j.asoc.2019.105742
Cruz-Reyes LFernandez ESanchez PCoello Coello CGomez C(2017)Incorporation of implicit decision-maker preferences in multi-objective evolutionary optimization using a multi-criteria classification methodApplied Soft Computing10.1016/j.asoc.2016.10.03750:C(48-57)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.asoc.2016.10.037
Liu RSong XFang LJiao L(2017)An r-dominance-based preference multi-objective optimization for many-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2098-x21:17(5003-5024)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s00500-016-2098-x
Li LYevseyeva IBasto-Fernandes VTrautmann HJing NEmmerich M(2017)Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 1017310.1007/978-3-319-54157-0_28(406-421)Online publication date: 19-Mar-2017
https://dl.acm.org/doi/10.1007/978-3-319-54157-0_28
Fernandez EGomez CRivera GCruz-Reyes L(2015)Hybrid metaheuristic approach for handling many objectives and decisions on partial support in project portfolio optimisationInformation Sciences: an International Journal10.1016/j.ins.2015.03.064315:C(102-122)Online publication date: 10-Sep-2015
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Reynoso-Meza GSanchis JBlasco XGarcía-Nieto S(2014)Physical programming for preference driven evolutionary multi-objective optimizationApplied Soft Computing10.1016/j.asoc.2014.07.00924:C(341-362)Online publication date: 1-Nov-2014
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Gong DSun JJi X(2013)Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problemsInformation Sciences: an International Journal10.1016/j.ins.2013.01.020233(141-161)Online publication date: 1-Jun-2013
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